Communication Behaviour-Based Big Data Application to Classify and Detect HTTP Automated Software
HTTP is recognized as the most widely used protocol on the Internet when applications are being transferred more and more by developers onto the web. Due to increasingly complex computer systems, diversity HTTP automated software (autoware) thrives. Unfortunately, besides normal autoware, HTTP malwa...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2016/2017373 |
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| _version_ | 1850178180996923392 |
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| author | Manh Cong Tran Yasuhiro Nakamura |
| author_facet | Manh Cong Tran Yasuhiro Nakamura |
| author_sort | Manh Cong Tran |
| collection | DOAJ |
| description | HTTP is recognized as the most widely used protocol on the Internet when applications are being transferred more and more by developers onto the web. Due to increasingly complex computer systems, diversity HTTP automated software (autoware) thrives. Unfortunately, besides normal autoware, HTTP malware and greyware are also spreading rapidly in web environment. Consequently, network communication is not just rigorously controlled by users intention. This raises the demand for analyzing HTTP autoware communication behaviour to detect and classify malicious and normal activities via HTTP traffic. Hence, in this paper, based on many studies and analysis of the autoware communication behaviour through access graph, a new method to detect and classify HTTP autoware communication at network level is presented. The proposal system includes combination of MapReduce of Hadoop and MarkLogic NoSQL database along with xQuery to deal with huge HTTP traffic generated each day in a large network. The method is examined with real outbound HTTP traffic data collected through a proxy server of a private network. Experimental results obtained for proposed method showed that promised outcomes are achieved since 95.1% of suspicious autoware are classified and detected. This finding may assist network and system administrator in inspecting early the internal threats caused by HTTP autoware. |
| format | Article |
| id | doaj-art-c4d222026e6d49dfb6eb6d37659ca443 |
| institution | OA Journals |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-c4d222026e6d49dfb6eb6d37659ca4432025-08-20T02:18:47ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552016-01-01201610.1155/2016/20173732017373Communication Behaviour-Based Big Data Application to Classify and Detect HTTP Automated SoftwareManh Cong Tran0Yasuhiro Nakamura1Department of Computer Science, National Defense Academy, 1-10-20 Hashirimizu, Yokosuka, Kanagawa 239-0811, JapanDepartment of Computer Science, National Defense Academy, 1-10-20 Hashirimizu, Yokosuka, Kanagawa 239-0811, JapanHTTP is recognized as the most widely used protocol on the Internet when applications are being transferred more and more by developers onto the web. Due to increasingly complex computer systems, diversity HTTP automated software (autoware) thrives. Unfortunately, besides normal autoware, HTTP malware and greyware are also spreading rapidly in web environment. Consequently, network communication is not just rigorously controlled by users intention. This raises the demand for analyzing HTTP autoware communication behaviour to detect and classify malicious and normal activities via HTTP traffic. Hence, in this paper, based on many studies and analysis of the autoware communication behaviour through access graph, a new method to detect and classify HTTP autoware communication at network level is presented. The proposal system includes combination of MapReduce of Hadoop and MarkLogic NoSQL database along with xQuery to deal with huge HTTP traffic generated each day in a large network. The method is examined with real outbound HTTP traffic data collected through a proxy server of a private network. Experimental results obtained for proposed method showed that promised outcomes are achieved since 95.1% of suspicious autoware are classified and detected. This finding may assist network and system administrator in inspecting early the internal threats caused by HTTP autoware.http://dx.doi.org/10.1155/2016/2017373 |
| spellingShingle | Manh Cong Tran Yasuhiro Nakamura Communication Behaviour-Based Big Data Application to Classify and Detect HTTP Automated Software Journal of Electrical and Computer Engineering |
| title | Communication Behaviour-Based Big Data Application to Classify and Detect HTTP Automated Software |
| title_full | Communication Behaviour-Based Big Data Application to Classify and Detect HTTP Automated Software |
| title_fullStr | Communication Behaviour-Based Big Data Application to Classify and Detect HTTP Automated Software |
| title_full_unstemmed | Communication Behaviour-Based Big Data Application to Classify and Detect HTTP Automated Software |
| title_short | Communication Behaviour-Based Big Data Application to Classify and Detect HTTP Automated Software |
| title_sort | communication behaviour based big data application to classify and detect http automated software |
| url | http://dx.doi.org/10.1155/2016/2017373 |
| work_keys_str_mv | AT manhcongtran communicationbehaviourbasedbigdataapplicationtoclassifyanddetecthttpautomatedsoftware AT yasuhironakamura communicationbehaviourbasedbigdataapplicationtoclassifyanddetecthttpautomatedsoftware |